Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Fetal Health Classification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 2,126 records of features extracted from Cardiotocography exams. Cardiotocograms are a simple and cost accessible option to assess fetal health. The equipment works by sending ultrasound pulses and reading its response, thus highlighting fetal heart rate, fetal movements, uterine contractions, and more. Three expert obstetricians classified the outcomes into three classes: Normal, Suspect, and Pathological.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 94.68%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 95.24%. When we processed the test dataset with the final model, the model achieved an accuracy score of 94.04%.

CONCLUSION: In this iteration, XGBoost appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Fetal Health Classification Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.kaggle.com/andrewmvd/fetal-health-classification

One source of potential performance benchmarks: https://www.kaggle.com/andrewmvd/fetal-health-classification/code

The HTML formatted report can be found here on GitHub.